Postdoctoral Fellow, Carnegie Mellon University
5000 Forbes Avenue
Gates Building 8125
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA, 15213
Postdoctoral Fellow, Carnegie Mellon University, 2013-present (w/ Christos Faloutsos)
Postdoctoral Reseacher, University of Massachusetts Amherst, 2011-2013 (w/ Don Towsley)
Ph.D. University of Massachusetts Amherst, 2010 (w/ Don Towsley)
Co-organized NetSci'14 Symposium Temporal Networks, Human Dynamics and Social Physics. Thanks to all the authors and invited speakers that made the symposium such a success (a really full auditorium)!
- Ting-Kai Huang, Bruno Ribeiro, Harsha V. Madhyastha, Michalis Faloutsos,
The Socio-monetary Incentives of Online Social Network Malware Campaigns, (accepted at COSN 2014) [please contact me for a preprint].
- Flavio Figueiredo, Jussara M Almeida, Yasuko Matsubara, Bruno Ribeiro, Christos Faloutsos,
Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries, (accetped at ECML/PKDD 2014) [arXiv:1405.1459].
How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate and model the effect of revisits on popularity. A revisit can be defined as a repeated play (song or video) or a re-tweet of the same hashtag over time. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of time series.
- Bruno Ribeiro, Christos Faloutsos,
Modeling Website Popularity Competition in the Attention-Activity Marketplace, [arXiv:1403.0600].
How does a new startup drive the popularity of competing websites into oblivion like Facebook famously did to MySpace? This question is of great interest to academics, technologists, and financial investors alike. In this work we exploit the singular way in which Facebook wiped out the popularity of MySpace, Hi5, Friendster, and Multiply to guide the design of a new popularity competition model. Our model provides new insights into what Nobel Laureate Herbert A. Simon called the "marketplace of attention," which we recast as the attention-activity marketplace. Our model design is further substantiated by user-level activity of 250,000 MySpace users obtained between 2004 and 2009. The resulting model not only accurately fits the observed Daily Active Users (DAU) of Facebook and its competitors but also predicts their fate four years into the future.
- Bruno Ribeiro,
Modeling and Predicting the Growth and Death of Membership-based Websites, WWW 2014 [pdf] [arXiv:1307.1354].
Carnegie Mellon University Press Release: In Age of Information Overload, Ability To Sustain Attention Determines Success | CMU SCS Press Release
How the Model Works commentary (last updated Feb 6 2014)
In the news: CNET | The Times of India | Pittsburgh Post Gazette | ACM TechNews | Phys.org news | Futurity.org | The Connectivist | The Tartan | Business Standard | Pittsburgh Business Times | Business Insider (I never said that "website audiences that grew via word-of-mouth are much better off in the long-run for retaining and engaging users", in fact my work has a number of counter-examples to this claim) | e! Science News | Network World | Science Daily News | TechAdvisor | MIT Technology Review: A roundup of the most interesting innovations coming out of research centers and universities
Driven by outstanding success stories of Internet startups such as Facebook and The Huffington Post, recent studies have thoroughly described their growth. These highly visible online success stories, however, overshadow an untold number of similar ventures that fail. The study of website popularity is ultimately incomplete without general mechanisms that can describe both successes and failures. In this work we present six years of the daily number of users (DAU) of twenty-two membership-based websites -- encompassing online social networks, grassroots movements, online forums, and membership-only Internet stores -- well balanced between successes and failures. We then propose a combination of reaction-diffusion-decay processes whose resulting equations seem not only to describe well the observed DAU time series but also provide means to roughly predict their evolution. This model allows an approximate automatic DAU-based classification of websites into self-sustainable v.s. unsustainable and whether the startup growth is mostly driven by marketing & media campaigns or word-of-mouth adoptions.
- (alphabetical) K. Avrachenkov, P. Basu, G. Neglia, B. Ribeiro*, and D. Towsley,
Pay Few, Influence Most: Online Myopic Network Covering, IEEE NetSciCom 2014 (Best Paper Award) [pdf].
Older version as Technical Report arXiv.
Efficient marketing or awareness-raising campaigns seek to recruit a small number, $w$, of influential individuals -- where $w$ is the campaign budget -- that are able to cover the largest possible target audience through their social connections.
To date, most of the related literature on maximizing this network cover assumes that the social network topology is known.
Even in such a case the coverage problem is NP-hard. In practice, the network topology is generally unknown and needs to be discovered on-the-fly.
In this paper we assume that the topology is gradually discovered thanks to recruited individuals disclosing their social connections.
Our goal is to provide an efficient online algorithm that recruits individuals so as to maximize the size of target audience covered by the campaign.
We analyze the performance of a variety of online myopic algorithms (i.e. that do not have a priori information on the topology) currently used to sample and search large networks, including the Flickr and YouTube networks.
We also propose a new greedy online algorithm, Maximum Expected Uncovered Degree (MEUD).
Our proposed algorithm greedily maximizes the expected size of the cover, but it requires the degree distribution to be known.
For a class of random power law networks we show that MEUD simplifies into a straightforward procedure, denoted as MOD because it requires only the knowledge of the Maximum Observed Degree.
We substantiate our analytical results with extensive simulations and show that MOD significantly outperforms all analyzed myopic algorithms.
- Bo Jiang, Liyuan Sun, Daniel R. Figueiredo, Bruno Ribeiro, Don Towsley,
On the duration and intensity of cumulative advantage competitions and the struggle of the fittest, [arXiv:1402.4536].
At the heart of competition for resources lie two simple principles: "survival of the fittest", which reflects how intrinsic fitnesses of competing agents influence their ability to gather resources; and "cumulative advantage", which reflects how accumulated resources help gather even more resources. In this letter we show that competitions embodying just the essence of these two principles exhibit a variety of surprising behaviors with respect to their duration, as measured by how long it takes for an indisputable winner to emerge, and intensity, as measured by how many times agents have equal amount of resources. We demonstrate that when the fitnesses of the agents are different, long-lasting competitions continue to occur in spite of a dramatic decrease in their intensity and the certainty that the fitter agent will win. We also unveil an unexpected phenomenon that we call "struggle of the fittest", where long-lasting competitions become more probable when one agent is slightly fitter than when agents are equally fit. These results have important implications for our understanding of competitions and various applications that leverage such models.
- Pinghui Wang, John C.S. Lui, Bruno Ribeiro, Don Towsley, Junzhou Zhao, Xiaohong Guan,
Efficiently Estimating Motif Statistics of Large Networks, accepted at TKDD 2014 [pdf].
Older ArXiv version arXiv:1306.5288 but there is a much improved new version about to come out.